Beyond samples: A metric revealing more connections of gut microbiota between individuals

Edinburgh postpartum depression scores are associated with vaginal and gut microbiota in pregnancy

BACKGROUND

Prenatal and postpartum depression may be influenced by the composition of host associated microbiomes. As such, the objective of this study was to elucidate the relationship between the human gut or vaginal microbiomes in pregnancy with prenatal or postpartum depression.

METHODS

140 female participants were recruited at their first prenatal visit and completed the Edinburgh Postnatal Depression Scale (EPDS) to screen for depression and anxiety, in addition the EPDS was completed one month postpartum. Vaginal and stool biospecimens were collected in the third trimester, analyzed using 16S rRNA gene sequencing, and assessed for alpha and beta diversity. Individual taxa differences and clustering using the k-medoids algorithm enabled community state type classification.

RESULTS

Participants with higher postpartum EPDS scores had higher species richness and lower abundance of L. crispatus in the vaginal microbiota compared to those with lower EPDS scores. Participants with a higher prenatal EPDS score had lower species richness of the gut microbiome. Participants with a vaginal community state type dominated by L. iners had the highest mean prenatal EPDS scores, whereas postpartum EPDS scores were similar regardless of prenatal vaginal state type.

LIMITATIONS

Our small sample size and participant's self-report bias limits generalizability of results.

CONCLUSIONS

Depression in the prenatal and postpartum period is associated with the composition and diversity of the gut and vaginal microbiomes in the third trimester of pregnancy. These results provide a foundational understanding of the microbial relationships between maternal health and depression for identifying potential therapeutic treatments.

Longitudinal Pilot Evaluation of the Gut Microbiota Comparing Patients With and Without Chronic Kidney Disease

OBJECTIVE

The gut microbiota contributes to metabolic diseases, such as diabetes and hypertension, but is poorly characterized in chronic kidney disease (CKD).

DESIGN AND METHODS

We enrolled 24 adults within household pairs, in which at least one member had self-reported kidney disease, diabetes, or hypertension. CKD was classified based on estimated glomerular filtration rate < 60 mL/min/1.73 m2 or urine-albumin-to-creatinine ratio of ≥ 30 mg/g. Participants collected stool and dietary recalls seasonally over a year. Gut microbiota was characterized using 16s rRNA and metagenomic sequencing.

RESULTS

Ten participants had CKD (42%) with a median (interquartile range) estimated glomerular filtration rate of 49 (44, 54) mL/min/1.73 m2. By 16s rRNA sequencing, there was moderate to high intraclass correlation (ICC = 0.63) for seasonal alpha diversity (Shannon index) within individuals and modest differences by season (P < .01). ICC was lower with metagenomics, which has resolution at the species level (ICC = 0.26). There were no differences in alpha or beta diversity by CKD with either method. Among 79 genera, Frisingicoccus, Tuzzerella, Faecalitalea, and Lachnoclostridium had lower abundance in CKD, while Collinsella, Lachnospiraceae_ND3007, Veillonella, and Erysipelotrichaceae_UCG_003 were more abundant in CKD (each nominal P < .05) using 16s rRNA sequencing. Higher Collinsella and Veillonella and lower Lachnoclostridium in CKD were also identified by metagenomics. By metagenomics, Coprococcus catus and Bacteroides stercoris were more and less abundant in CKD, respectively, at false discovery rate corrected P = .02.

CONCLUSIONS

We identified candidate taxa in the gut microbiota associated with CKD. High ICC in individuals with modest seasonal impacts implies that follow-up studies may use less frequent sampling.

Bacterial gut microbiome differences in adults with ADHD and in children with ADHD on psychostimulant medication

Recent evidence suggests that there is a link between neurodevelopmental disorders, such as attention deficit hyperactivity disorder (ADHD), and the gut microbiome. However, most studies to date have had low sample sizes, have not investigated the impact of psychostimulant medication, and have not adjusted for potential confounders, including body mass index, stool consistency and diet. To this end, we conducted the largest, to our knowledge, fecal shotgun metagenomic sequencing study in ADHD, with 147 well-characterized adult and child patients. For a subset of individuals, plasma levels of inflammatory markers and short-chain fatty acids were also measured. In adult ADHD patients (n=84), compared to controls (n=52), we found a significant difference in beta diversity both regarding bacterial strains (taxonomic) and bacterial genes (functional). In children with ADHD (n=63), we found that those on psychostimulant medication (n=33 on medication vs. n=30 not on medication) had (i) significantly different taxonomic beta diversity, (ii) lower functional and taxonomic evenness, (iii) lower abundance of the strain Bacteroides stercoris CL09T03C01 and bacterial genes encoding an enzyme in vitamin B₁₂ synthesis, and (iv) higher plasma levels of vascular inflammatory markers sICAM-1 and sVCAM-1. Our study continues to support a role for the gut microbiome in neurodevelopmental disorders and provides additional insights into the effects of psychostimulant medication. However, additional studies are needed to replicate these findings and examine causal relationships with the disorder.

Exploring methods to summarize gut microbiota composition for microbiability estimation and phenotypic prediction in swine

The microbial composition resemblance among individuals in a group can be summarized in a square covariance matrix and fitted in linear models. We investigated eight approaches to create the matrix that quantified the resemblance between animals based on the gut microbiota composition. We aimed to compare the performance of different methods in estimating trait microbiability and predicting growth and body composition traits in three pig breeds. This study included 651 purebred boars from either breed: Duroc (n = 205), Landrace (n = 226), and Large White (n = 220). Growth and body composition traits, including body weight (BW), ultrasound backfat thickness (BF), ultrasound loin depth (LD), and ultrasound intramuscular fat (IMF) content, were measured on live animals at the market weight (156 ± 2.5 days of age). Rectal swabs were taken from each animal at 158 ± 4 days of age and subjected to 16S rRNA gene sequencing. Eight methods were used to create the microbial similarity matrices, including four kernel functions (Linear Kernel, LK; Polynomial Kernel, PK; Gaussian Kernel, GK; Arc-cosine Kernel with one hidden layer, AK1), two dissimilarity methods (Bray-Curtis, BC; Jaccard, JA), and two ordination methods (Metric Multidimensional Scaling, MDS; Detrended Correspondence analysis, DCA). Based on the matrix used, microbiability estimates ranged from 0.07 to 0.21 and 0.12 to 0.53 for Duroc, 0.03 to 0.21 and 0.05 to 0.44 for Landrace, and 0.02 to 0.24 and 0.05 to 0.52 for Large White pigs averaged over traits in the model with sire, pen, and microbiome, and model with the only microbiome, respectively. The GK, JA, BC, and AK1 obtained greater microbiability estimates than the remaining methods across traits and breeds. Predictions were made within each breed group using four-fold cross-validation based on the relatedness of sires in each breed group. The prediction accuracy ranged from 0.03 to 0.18 for BW, 0.08 to 0.31 for BF, 0.21 to 0.48 for LD, and 0.04 to 0.16 for IMF when averaged across breeds. The BC, MDS, LK, and JA achieved better accuracy than other methods in most predictions. Overall, the PK and DCA exhibited the worst performance compared to other microbiability estimation and prediction methods. The current study shows how alternative approaches summarized the resemblance of gut microbiota composition among animals and contributed this information to variance component estimation and phenotypic prediction in swine.